Autonomous battery optimisation by deploying distributed experiments and simulations

Non-trivial relationships link individual materials properties to device-level performance. Device optimisation therefore calls for new automation approaches beyond the laboratory bench with tight integration of different research methods. We demonstrate a Materials Acceleration Platform (MAP) in the field of battery research based on our problem-agnostic Fast Intentional Agnostic Learning Server (FINALES) framework, which integrates simulations and physical experiments without centrally controlling them. The connected capabilities entail the formulation and characterisation of electrolytes, cell assembly and testing, early lifetime prediction, and ontology-mapped data storage provided by institutions distributed across Europe. The infrastructure is used to optimise the ionic conductivity of electrolytes and the End Of Life (EOL) of lithium-ion coin cells by varying the electrolyte formulation. We rediscover trends in ionic conductivity and investigate the effect of the electrolyte formulation on the EOL. We further demonstrate the capability of our MAP to bridge diverse research modalities, scales, and institutions enabling system-level investigations under asynchronous conditions while handling concurrent workflows on the material- and system-level, demonstrating true intention-agnosticism.